Conversable Launches AQUA 2.0



Conversable has launched AQUA 2.0 (Answer Queries Using AI), an adaptive response bot system that now includes three tiers of query management: Basic, Care, and Engage.

Following a successful release of AQUA 1.0 in March, Conversable has invested heavily to expand the capabilities of the system.

Using machine learning, natural language processing, and pre-trained data sets, AQUA 2.0 allows companies to improve throughput of brand voice, answer FAQs, and engage with customers at scale on the platforms of their choice.

The Basic version helps companies build in brand personality and build conversation that flows naturally using pre-trained responses to common interactions. Care uses natural language processing and machine learning to answer the most frequently asked questions, both known and unknown. Engage focuses on customer engagement at scale using machine learning and custom business rules, automatically with an unlimited number of predefined responses.

"This is the only bot platform that can do this much right out of the box," said Ben Lamm, CEO of Conversable, in a statement. "It's about time we stopped treating bots like science experiments and started shipping high-ROI bots at the speed of business. That's exactly why we built AQUA. If you want to be up and running in a matter of weeks, we should be your first call."

"The Achilles heel of the bot ecosystem today is that the one-size-fits-all model gives too much of everything and not enough of the right things," said Andrew Busey, co-founder and chief product officer at Conversable, in a statement. "AQUA is the first product that actually allows you to pay for what you need and add on later. It's the adaptability that the businesses of today want and require."

Conversable also provides Insights and Training tools. Insights provides a real-time, bird's eye view of conversational experiences and allows users to gain insight into which topics are popular and yielding good outcomes. The AQUA training tool lets companies interact directly with each AQUA product, simulate interactions to see how the system will respond, and validate answers to ensure the system is responding properly. Machine learning helps suggest appropriate answers from the existing knowledge base and identify queries that do not have relevant responses yet.